Whether language and mathematics rely on shared or distinct neural representations remains an unresolved question in cognitive neuroscience. Here we combine latent features from a large language model (LLM) with vertex-wise encoding models to examine cross-domain generalization between language and mathematics. Thirty-two participants performed sentence comprehension and calculation tasks during fMRI, and encoding models were trained using features embedded in a common latent space. Cross-domain prediction identified cortical regions associated with partially shared representations, most prominently the left 55b, while control analyses suggested that these effects could not be fully explained by low-level visual processing or simple task-general factors. Task-specificity contrasts revealed stronger language-related prediction in the left anterior superior temporal and angular gyri and math-related prediction in the left precentral and intraparietal sulci. Model-weight analyses further showed that shared and domain-specific prediction patterns were reflected in distinct weight profiles across cortical regions. Connectivity analyses showed task-dependent functional coupling between cross-domain regions and language- or math-related networks. Together, these findings suggest that language and mathematics involve partially shared neural representations alongside domain-specific cortical organization, helping reconcile previous contrasting views on their neural basis.
Nakai, T., Kubo, T., Nishimoto, S.
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